This is a sentiment analysis experiment on Gaussian Naive-Bayes classifiers, just to give them a try and see how they work. Dataset information is given below. Was made by using Pandas to organize dataset and generate scores, in which common words were then filtered out. After that, numerical labels were placed on words and fed through a Gaussian Naive-Bayes classifier ================================================================================ This dataset was created for the Paper 'From Group to Individual Labels using Deep Features', Kotzias et. al,. KDD 2015 Please cite the paper if you want to use it :) It contains sentences labelled with positive or negative sentiment, extracted from reviews of products, movies, and restaurants ======= Format: ======= sentence \t score \n ======= Details: ======= Score is either 1 (for positive) or 0 (for negative) The sentences come from three different websites/fields: imdb.com amazon.com yelp.com For each website, there exist 500 positive and 500 negative sentences. Those were selected randomly for larger datasets of reviews. We attempted to select sentences that have a clearly positive or negative connotaton, the goal was for no neutral sentences to be selected. For the full datasets look: imdb: Maas et. al., 2011 'Learning word vectors for sentiment analysis' amazon: McAuley et. al., 2013 'Hidden factors and hidden topics: Understanding rating dimensions with review text' yelp: Yelp dataset challenge http://www.yelp.com/dataset_challenge